Asset value data determination method and device, electronic equipment and readable storage medium

Abstract

The invention relates to the technical field of data processing, and provides an asset value data determination method and device, electronic equipment and a readable storage medium. The method comprises the following steps: standardizing house attribute data to obtain standardized house attribute data; performing rationality verification processing on the standardized house attribute data to obtain effective house attribute data; performing multi-factor fusion processing on the effective house attribute data, preset asset static parameters and preset asset dynamic parameters to obtain target house asset value data; and sending the house target asset value data to the target terminal device for display, thereby improving the consistency and normalization of heterogeneous data, improving the data quality and authenticity, enhancing the response speed of asset value evaluation to market dynamics, improving the accuracy and comparability of a pricing result, and improving the user experience. And the decision transparency and the business execution efficiency are improved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and readable storage medium for determining asset value data. Background Technology

[0002] In housing rental management, the conventional approach is to collect and price housing information manually or through systems. This results in fragmented information, delayed updates, and pricing often based on static factors such as area and orientation. Consequently, prices fail to accurately reflect the value of the housing and market supply and demand, leading to low rental efficiency and resource imbalance. Furthermore, relying on manual experience to integrate information and set prices is inefficient, subjective, and difficult to scale. On the other hand, management systems with fixed rules can input basic information and preset price ranges, but the rigid rules cause prices to deviate from actual operational needs.

[0003] It is evident that existing technologies suffer from problems such as fragmented and error-prone data due to manual or static rule-based information collection, delayed updates, and reliance solely on static factor pricing, resulting in poor adaptability of housing prices, low accuracy of resource allocation, and poor versatility. Summary of the Invention

[0004] In view of this, the present disclosure provides a method, apparatus, electronic device and readable storage medium for determining asset value data, in order to solve the problems in the prior art where information is collected manually or according to static rules, resulting in fragmented and error-prone data, delayed updates, and reliance on static factor pricing, leading to poor adaptability of housing prices, low accuracy of resource allocation and poor versatility.

[0005] A first aspect of this disclosure provides a method for determining asset value data, comprising: standardizing housing attribute data to obtain standardized housing attribute data; performing reasonableness verification on the standardized housing attribute data to obtain valid housing attribute data; performing multi-factor fusion processing on the valid housing attribute data, preset asset static parameters, and preset asset dynamic parameters to obtain target housing asset value data; and sending the target housing asset value data to a target terminal device for display.

[0006] In some embodiments, the effective housing attribute data, preset asset static parameters, and preset asset dynamic parameters are subjected to multi-factor fusion processing to obtain target housing asset value data, including: performing dynamic weight mapping processing on the effective housing attribute data to obtain a dynamic weight vector; performing weighted fusion processing on the dynamic weight vector and preset asset static parameters to obtain a fused asset value; and performing reweighted processing on the fused asset value and preset asset dynamic parameters to obtain target housing asset value data.

[0007] In some embodiments, the standardized housing attribute data is subjected to a reasonableness verification process to obtain valid housing attribute data, including: performing historical valuation alignment processing on the standardized housing attribute data to obtain a housing asset estimate; performing difference calculation processing on the housing asset estimate and the standardized housing attribute data to obtain a verification result; and filtering the standardized housing attribute data based on the verification result to obtain valid housing attribute data.

[0008] In some embodiments, the effective housing attribute data, preset asset static parameters, and preset asset dynamic parameters are subjected to multi-factor fusion processing to obtain target housing asset value data, including: performing interactive feature generation processing on the effective housing attribute data to obtain the cross coefficients corresponding to the effective housing attribute data; fusing the cross coefficients and preset asset static parameters to obtain static correction values; and further fusing the static correction values ​​and preset asset dynamic parameters to obtain target housing asset value data.

[0009] In some embodiments, the housing attribute data is standardized to obtain standardized housing attribute data, including: performing missing value completion processing on the housing attribute data to obtain completed attribute data; and performing format standardization processing on the completed attribute data to obtain standardized housing attribute data.

[0010] In some embodiments, after sending the target asset value data of the house to the target terminal device for display, the method further includes: obtaining real-time feedback data; performing closed-loop self-learning processing on the real-time feedback data and the target house asset value data to obtain asset dynamic update parameters; and performing multi-factor fusion processing on the asset dynamic update parameters, valid house attribute data, and preset asset static parameters to obtain house asset value update data.

[0011] In some embodiments, sending the target property asset value data to a target terminal device for display includes: performing message encapsulation processing on the target property asset value data to obtain encapsulated data; performing encoding processing on the encapsulated data to obtain encoded data; and sending the encoded data to the target terminal device for display through a preset secure channel.

[0012] A second aspect of this disclosure provides an asset value data determination device, comprising: a first processing module for standardizing housing attribute data to obtain standardized housing attribute data; a second processing module for performing reasonableness verification processing on the standardized housing attribute data to obtain valid housing attribute data; a third processing module for performing multi-factor fusion processing on the valid housing attribute data, preset asset static parameters, and preset asset dynamic parameters to obtain target housing asset value data; and a fourth processing module for sending the target housing asset value data to a target terminal device for display.

[0013] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0014] A fourth aspect of this disclosure provides a readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0015] The beneficial effects of this disclosed embodiment compared with the prior art are as follows: Standardized housing attribute data is obtained by standardizing housing attribute data; valid housing attribute data is obtained by performing rationality verification on the standardized housing attribute data; target housing asset value data is obtained by multi-factor fusion processing of the valid housing attribute data, preset static asset parameters, and preset dynamic asset parameters; and the target housing asset value data is sent to the target terminal device for display. Thus, through standardization and rationality verification, the real-time nature of information acquisition is ensured. The introduction of dynamic asset parameters enhances the flexibility and timeliness of asset value data determination, improves data quality and consistency, enhances responsiveness to market dynamics, improves the accuracy and real-time nature of asset valuation, and increases information push efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure; Figure 2 This is a flowchart illustrating a method for determining asset value data provided in an embodiment of this disclosure; Figure 3 This is a flowchart illustrating another method for determining asset value data provided in this embodiment of the disclosure; Figure 4 This is a schematic diagram of the structure of an asset value data determination device provided in an embodiment of this disclosure; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of this disclosure. However, those skilled in the art will understand that this disclosure may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this disclosure with unnecessary detail.

[0019] It should be noted that the user information (including but not limited to terminal device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0020] The following will describe in detail, with reference to the accompanying drawings, a method and apparatus for determining asset value data according to an embodiment of the present disclosure.

[0021] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure. The application scenario may include terminal devices 1, 2, and 3, server 4, and network 5.

[0022] Terminal devices 1, 2, and 3 can be hardware or software. When terminal devices 1, 2, and 3 are hardware, they can be various electronic devices with displays and supporting communication with server 4, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 1, 2, and 3 are software, they can be installed in the aforementioned electronic devices. Terminal devices 1, 2, and 3 can be implemented as multiple software programs or software modules, or as a single software program or software module; this disclosure does not limit this. Furthermore, various applications can be installed on terminal devices 1, 2, and 3, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

[0023] Server 4 can be a server that provides various services, such as a backend server that receives requests sent by terminal devices with which it has established communication connections. This backend server can receive and analyze the requests sent by the terminal devices and generate processing results. Server 4 can be a single server, a server cluster consisting of several servers, or a cloud computing service center. This disclosure embodiment does not limit this.

[0024] It should be noted that server 4 can be either hardware or software. When server 4 is hardware, it can be various electronic devices that provide various services to terminal devices 1, 2, and 3. When server 4 is software, it can be multiple software programs or software modules that provide various services to terminal devices 1, 2, and 3, or it can be a single software program or software module that provides various services to terminal devices 1, 2, and 3. This disclosure does not limit the scope of the embodiments.

[0025] Network 5 can be a wired network using coaxial cable, twisted pair, and fiber optic connection, or it can be a wireless network that enables interconnection of various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), and Infrared. This disclosure does not limit the scope of the network.

[0026] Users can establish a communication connection with server 4 via network 5 through terminal devices 1, 2, and 3 to receive or send information. Specifically, server 4 can obtain house attribute data through terminal devices 1, 2, and 3, standardize the house attribute data to obtain standardized house attribute data, perform reasonableness verification on the standardized house attribute data to obtain valid house attribute data, perform multi-factor fusion processing on the valid house attribute data, preset asset static parameters, and preset asset dynamic parameters to obtain target house asset value data, and send the target house asset value data to the target terminal device for display.

[0027] It should be noted that the specific types, quantities, and combinations of terminal devices 1, 2, and 3, server 4, and network 5 can be adjusted according to the actual needs of the application scenario, and this disclosure embodiment does not impose any restrictions on this.

[0028] Figure 2 This is a flowchart illustrating a method for determining asset value data provided in an embodiment of this disclosure. Figure 2 The method for determining asset value data can be by Figure 1 The server executes this. For example... Figure 2 As shown, the method for determining the asset value data includes: S201, standardize the housing attribute data to obtain standardized housing attribute data.

[0029] Specifically, property attribute data can be a collection of information obtained from a data source that can be used to characterize various aspects of a property. Property attribute data can include the static attributes and dynamic status of a property, such as the building identification, floor location, room number, building area, orientation, current rental status, and / or vacancy duration, etc. There are no restrictions here. Property attribute data can be input data for asset valuation and rental price calculation. This property attribute data can be raw, unprocessed source data that can be manually entered by staff through the front-end property information entry interface or indirectly collected by monitoring the property's usage status through smart devices connected to the property.

[0030] Standardization processing is a data preprocessing procedure that cleans, transforms, and / or unifies the format of acquired housing attribute data according to predefined rules and standards. Through standardization, heterogeneous data from different sources, in different formats, or with different expressions can be transformed into standardized data with a clear structure and semantics, usable by subsequent models or rules. This eliminates data ambiguity and ensures data quality consistency. Standardization processing can receive housing attribute data from various data collection channels. This data can be represented in different formats or units; for example, orientation can be recorded as "south," "facing south," or "S," area units can be "square meters" or "㎡," and vacancy duration can be represented as "30 days" or "one month." It can be processed based on a predefined standard data dictionary and transformation rules. For example, the data dictionary can specify the standard name, data type, value range, and / or standard value domain for each attribute field; transformation rules can define how to map raw values ​​to standard values. For instance, text representing "facing south" can be uniformly converted to the standard enumeration value "SOUTH," and area values ​​can be uniformly converted to floating-point numbers in "square meters."

[0031] Furthermore, the standardization process can also include data verification steps. For example, it can check whether the room number conforms to the predefined coding rules, check whether the area value is a reasonable positive number, and if outliers or missing values ​​are found, they can be filled, corrected or marked according to the preset processing strategy. Then, the house attribute data can be parsed and transformed according to the predefined data dictionary and transformation rules. The cleaned and transformed data items can be organized based on a unified structured format to generate standardized house attribute data.

[0032] For example, in an apartment management system, housing attribute data can be obtained through various data entry ports and smart meter interfaces. The building information in this data may be recorded as "Building 1" or "Building 1"; the orientation information may be "South-facing" or "N"; and the area data may include the unit "square meters" or only be numbers. A standardized processing flow can be used, based on a pre-set standard data dictionary, to map "Building 1" and "Building 1" to the standard building code "B001," and "South-facing" and "N" to the standard orientation code "S." The area numbers can be uniformly parsed and stored as values ​​in "square meters," and the room number format can be validated to ensure it conforms to the rule of "combination of floor number and room number," thus obtaining standardized housing attribute data.

[0033] This application embodiment maps housing attribute data into a standardized data format according to a predefined data dictionary and conversion rules through cleaning, transformation, format unification and / or data verification. It fills, corrects or marks abnormal or missing values ​​to generate standardized housing attribute data with a unified structure and clear semantics. This improves the consistency and standardization of multi-source heterogeneous data, eliminates the ambiguity and error risks caused by different data formats, units or expressions, and improves the accuracy and reliability of the asset value data determination process.

[0034] S202, perform a rationality verification process on the standardized housing attribute data to obtain valid housing attribute data.

[0035] Specifically, the rationality verification process can be a verification and screening process performed on standardized housing attribute data according to preset business rules and logical conditions. This can determine whether the data is true, valid and in line with common sense. Through the rationality verification process, errors, contradictions, omissions or records that do not conform to the actual situation in the standardized housing attribute data can be identified and eliminated, ensuring the authenticity of the retained data.

[0036] Furthermore, the rationality verification process can specifically include data integrity verification, numerical range verification, and logical consistency verification. Among them, data integrity verification can be used to confirm whether key fields of each property record are missing, such as whether fields such as building number, room number, and area are empty; numerical range verification can be used to determine whether the values ​​of numerical fields are within a reasonable physical or business range, such as whether the area value is positive and does not exceed the building design limit, and whether the vacancy duration is non-negative, etc., without any restrictions here; logical consistency verification can be used to analyze whether there are contradictions in the logical relationships between different fields. For example, if the rental status is marked as "rented", its associated vacancy duration should be zero or a specific flag value. If a long vacancy period is recorded at the same time, it can be considered a logical conflict.

[0037] Valid housing attribute data can be standardized housing attribute data that has been deemed qualified and usable through reasonableness verification. This valid housing attribute data can be used as input data for models or algorithms that generate rental prices.

[0038] For example, in an apartment management system, standardized housing attribute data can include information such as the apartment building's unit number, area, orientation, rental status, and vacancy duration. Data integrity checks can then be performed by scanning all records, filtering out rows with missing key fields such as room number and area, and marking them. Numerical range checks can be performed to verify that the area field is always positive and within a preset reasonable range. The vacancy duration field can be verified to be non-negative. Logical consistency checks can be performed by comparing the "rental status" and "vacancy duration" of each record. Records with a status of "rented" but a vacancy duration greater than zero, or records with a status of "vacant" but a vacancy duration of zero without special explanation, can be identified as logical anomalies. Standardized housing attribute data that passes all the above checks without any anomaly indicators is considered valid housing attribute data.

[0039] This application's embodiments supplement missing key fields in standardized housing attribute data through data integrity verification, eliminate or repair out-of-limit and negative values ​​through numerical range verification, and remove mutually exclusive records of rental status and vacancy duration through logical consistency verification, thereby obtaining valid housing attribute data. By introducing systematic rationality verification rules including data integrity, numerical range, and logical consistency verification, the standardized housing data is quality-screened, identifying and filtering out invalid data entries with missing, erroneous, or logically contradictory information. This improves the quality of valid housing attribute data, enhances the accuracy and reliability of information used for pricing analysis, avoids distortion of asset value data, improves data integrity and authenticity, and enhances the accuracy of asset value data determination.

[0040] S203 involves multi-factor fusion processing of valid housing attribute data, preset asset static parameters, and preset asset dynamic parameters to obtain target housing asset value data.

[0041] Specifically, the preset static parameters of the asset can be pre-set quantitative coefficients or benchmark values ​​that remain relatively fixed within a certain period. These preset static parameters can be used to characterize the fundamental and long-term factors that affect the asset value, including but not limited to the base price, the area influence weight, and / or the orientation adjustment coefficient. This provides a stable value calculation framework and benchmark for multi-factor fusion processing, ensuring that the value assessment is consistent and comparable. The preset dynamic parameters of the asset can be pre-set parameters whose specific values ​​or degree of influence can be dynamically adjusted according to the real-time status of the property. For example, the vacancy duration adjustment coefficient can be adjusted as the number of vacant days increases.

[0042] In addition, multi-factor fusion processing can be a data processing process that integrates and calculates different types and dimensions of input data and parameters according to predetermined calculation rules. Specifically, it can combine dispersed static attributes, dynamic states and preset rules through weighting, coefficient adjustment and formula calculation to generate comprehensive quantitative value results, namely target housing asset value data.

[0043] Furthermore, multi-factor fusion processing can specifically include sub-steps such as data alignment and parameter extraction, weighted calculation and coefficient adjustment. Data alignment can be used to ensure that data from different sources can be matched based on the same property identifier, while parameter extraction can dynamically determine the specific values ​​of preset asset dynamic parameters based on the current valid property attribute data.

[0044] For example, in an apartment management system, valid property attribute data for a target room can be read from the property information database. This includes information such as being located in Building 2, on the 10th floor, room number 1002, a building area of ​​50 square meters, facing south, and currently listed as available for rent and vacant for 15 days. Preset static asset parameters can be retrieved, such as a base rent of a certain amount per square meter for the area, an area weighting coefficient of W1, and a south-facing orientation coefficient of 1.1. Simultaneously, based on preset rules (e.g., an adjustment coefficient of 1 for vacancy within 30 days, and a decrease of a certain percentage every 10 days after 30 days), combined with the current 15-day vacancy information, the preset dynamic asset parameters can be used to determine that the vacancy duration adjustment coefficient can be 1. Furthermore, multi-factor fusion processing can be performed, combining static factors such as the base rent, area multiplied by area weight, and orientation coefficient, and multiplying by the vacancy duration adjustment coefficient (i.e., adjusting dynamic factors). Through a preset calculation formula, the target property asset value data for the room, i.e., the suggested rental price, can be obtained.

[0045] In addition, the target property asset value data can be obtained through multi-factor fusion processing, which can be used to represent the comprehensive quantitative value of a specific property in its current state, or it can be used to represent the suggested asset price or rent.

[0046] This application embodiment performs multi-factor fusion processing on valid housing attribute data, preset asset static parameters, and preset asset dynamic parameters. It matches the housing identifier with the base price, area influence weight, and orientation adjustment coefficient in the preset asset static parameters, and dynamically determines the vacancy duration adjustment coefficient in the preset asset dynamic parameters based on the current vacancy duration. Through weighted calculation and coefficient adjustment, the target housing asset value data is obtained. This improves the comprehensiveness and accuracy of value assessment. Quantifying operational indicators such as vacancy duration into the calculation through preset asset dynamic parameters helps optimize asset turnover and revenue management, enhances the synchronous response capability of asset value data to static attributes and real-time status, improves the consistency between valuation results and market reality, and enhances the flexibility and comparability of asset pricing.

[0047] S204, send the target asset value data of the house to the target terminal device for display.

[0048] Specifically, the target terminal device can be a computing device used to receive and present the target asset value data of a property. It can be a management terminal used by rental business personnel or a user device corresponding to an online rental platform accessed by potential tenants, without any limitation here. The target terminal device can present the received target asset value data of the property to the user in the form of graphics, text, lists and / or charts through a display screen or graphical user interface. The content displayed can be derived from the target asset value data of the property transmitted in the sending step, and the display form and layout can be determined by the preset rules of the application or web interface running on the target terminal device.

[0049] Alternatively, a server or cloud system that has deployed the housing rental method can transmit data packets via a network communication protocol based on a preset target terminal device address or session identifier; the data packet can encapsulate the target property value data and its associated housing identifier information.

[0050] This application embodiment achieves effective transmission and transparency of pricing results to the business execution terminal by sending the calculated target asset value data of the house to the target terminal device and displaying it. This ensures that the calculation results of the scientific pricing model can be applied to the front end of the rental business, providing rental personnel with a clear pricing basis. It enables the asset value dynamically generated based on multi-dimensional information to quickly and accurately reach the decision-making end and the demand end, improving the smoothness and efficiency of the house asset value determination process.

[0051] According to the technical solution provided in this disclosure, by inputting standardized housing attribute data, the data is sequentially cleaned, converted, formatted, and validated to map it into standardized housing attribute data. This data undergoes a rationality check, which involves filling in missing fields through data integrity checks, removing outliers through numerical range checks, and clearing mutually exclusive records of rental status and vacancy duration through logical consistency checks, outputting valid housing attribute data. This valid housing attribute data is then fused with preset static and dynamic asset parameters through multi-factor fusion processing, followed by weighted calculation and coefficient adjustment to obtain the target housing asset value data. Finally, the target housing asset value data is packaged and sent to the target terminal device for display. This improves the consistency and standardization of heterogeneous data, enhances data quality and authenticity, strengthens the responsiveness of asset valuation to market dynamics, improves the accuracy and comparability of pricing results, and increases decision-making transparency and business execution efficiency.

[0052] In some embodiments, the effective housing attribute data, preset asset static parameters, and preset asset dynamic parameters are subjected to multi-factor fusion processing to obtain target housing asset value data, including: performing dynamic weight mapping processing on the effective housing attribute data to obtain a dynamic weight vector; performing weighted fusion processing on the dynamic weight vector and preset asset static parameters to obtain a fused asset value; and performing reweighted processing on the fused asset value and preset asset dynamic parameters to obtain target housing asset value data.

[0053] Specifically, dynamic weight mapping processing can be a data processing procedure that uses the values ​​of specific dynamic fields (such as vacancy duration) in valid property attribute data and a preset mapping rule to obtain a dynamic weight vector. This process can transform the dynamic state of a property (such as vacancy status) into weights that affect the importance of other attributes (such as orientation and area) in value calculation, enhancing the dynamic adaptability of value assessment. The dynamic weight vector can be a numerical sequence, where each element corresponds to a weight ratio that a property attribute (or static parameter) should occupy in subsequent calculations. This quantifies the dynamic state of a property into operable weight information. The dynamic weight vector can be used to adjust the influence of static parameters in value calculation. For example, the longer the vacancy duration, the lower the weight of attributes such as area or orientation can be mapped to in order to encourage the property to be rented out as soon as possible, thereby reducing the overall base valuation.

[0054] In addition, weighted fusion processing can be a data processing process that multiplies one set of data with another set of corresponding weights and then sums them to obtain a fused value. In this way, the basic asset value can be calculated by adjusting the preset static parameters of each asset through dynamic weight vectors. The asset value fusion value can be the output result of weighted fusion processing. This asset value fusion value can be used to characterize the value contributed by the static attributes of the house under a specific dynamic state (reflected by the weight vector).

[0055] Furthermore, reweighting can be a data processing procedure that combines a numerical value with a weighting coefficient (or adjustment function) to amplify, reduce, or non-linearly adjust that value. This allows for a secondary adjustment of the initial asset value fusion. This adjustment can be achieved by using preset asset dynamic parameters (such as an vacancy duration adjustment coefficient) that characterize market or operational urgency to obtain a target value that aligns with the current market strategy. For example, the asset value fusion can be multiplied by an adjustment coefficient determined by the vacancy duration (this coefficient is less than or equal to 1). The longer the vacancy period, the smaller the adjustment coefficient, and the lower the target price.

[0056] For example, valid property attribute data for the target room can be obtained, including its location on floor F2 of building 1, area as preset, south-facing, current status as available for rent, and vacancy duration of 30 days. Dynamic weight mapping can be applied to the 30-day vacancy duration, generating a dynamic weight vector containing area and orientation weights based on preset mapping rules. Since the vacancy duration is moderate, the area weight can be mapped to a moderate value, while the favorable south-facing orientation weight can be mapped to a relatively high value still affected by vacancy. The dynamic weight vector can be weighted and fused with preset static asset parameters (including benchmark price per unit area, area weight coefficient, and south-facing coefficient) to calculate a fused asset value. This fused asset value initially combines the impact of room area, orientation, and current vacancy status on the corresponding value contribution. Based on the 30-day vacancy duration again, preset dynamic asset parameters, i.e., vacancy duration adjustment coefficient, can be determined. The fused asset value can then be reweighted (e.g., multiplied) with this adjustment coefficient to obtain the target property asset value data for the target room, i.e., the suggested rental price.

[0057] According to the technical solution provided in this disclosure, dynamic weight mapping processing is performed on valid housing attribute data. A dynamic weight vector is generated based on preset mapping rules, using vacancy duration as input, to quantify the weight adjustment of area and orientation attributes by the vacancy status. The dynamic weight vector is then weighted and fused with preset asset static parameters to obtain a fused asset value, forming the basic value of static attributes in the current vacancy situation. The fused asset value is then reweighted with preset asset dynamic parameters, and the basic value is scaled a second time using a vacancy duration adjustment coefficient to obtain the target housing asset value data. This improves the sensitivity of valuation to dynamic vacancy status, enhances the flexibility and market adaptability of pricing strategies, strengthens asset turnover efficiency and revenue controllability, and improves the accuracy and practicality of asset valuation.

[0058] In some embodiments, the standardized housing attribute data is subjected to a reasonableness verification process to obtain valid housing attribute data, including: performing historical valuation alignment processing on the standardized housing attribute data to obtain a housing asset estimate; performing difference calculation processing on the housing asset estimate and the standardized housing attribute data to obtain a verification result; and filtering the standardized housing attribute data based on the verification result to obtain valid housing attribute data.

[0059] Specifically, historical valuation alignment processing involves processing standardized housing attribute data using historical housing asset valuations to generate a preliminary asset value estimate. Discrepancy calculation processing involves comparing and analyzing the housing asset estimate obtained from historical valuation alignment with the valuation-related information in the current standardized housing attribute data to quantify the degree of inconsistency. This generates objective quantitative indicators to determine the deviation between the current standardized data and the historical valuation benchmark, providing a basis for judging data validity.

[0060] Furthermore, the calculation of discrepancy can be represented by calculating a discrepancy score. For example, based on core value-influencing factors such as "area" and "orientation" in standardized housing attribute data, combined with the current market unit price parameter, an immediate reference value can be calculated. This immediate reference value can be compared with the estimated value of the housing asset, and the relative error or absolute difference can be calculated as the discrepancy score. This discrepancy score is the verification result. The higher the score, the greater the deviation between the current standardized data and the expectation, and the higher the possibility of an anomaly. For example, for a target room, based on its area and south-facing attributes in its standardized data, combined with the current benchmark rental unit price of the apartment, an immediate reference monthly rent can be calculated. This immediate reference monthly rent can be compared with the estimated value of the housing asset. If the two are close, the discrepancy score is low; if there is a significant difference, the discrepancy score is high.

[0061] Furthermore, the filtering process can be used to set a judgment threshold for the verification results generated based on the difference degree calculation, and to filter the input standardized housing attribute dataset, retaining reasonable data and removing abnormal data. Further, a difference degree threshold can be preset, and the verification result corresponding to the standardized housing data of each property can be compared with the preset difference degree threshold. If the difference degree score is less than the preset difference degree threshold, the standardized housing attribute data is reasonable, and the standardized housing attribute data is retained and marked as valid housing attribute data; if the difference degree score is greater than or equal to the threshold, the standardized housing attribute data has a recording error, attribute abnormality, or special circumstances, and the standardized housing attribute data is deleted from the data pool currently used for price calculation.

[0062] According to the technical solution provided in this disclosure, by performing historical valuation alignment processing, a housing asset estimate is generated from historical housing asset valuations; the housing asset estimate and standardized housing attribute data are compared and the degree of difference is calculated to quantify the degree of deviation and obtain a verification result; based on the verification result, the standardized housing attribute data is filtered, and only data with a difference score lower than a preset difference threshold is retained as valid housing attribute data. In this way, the objectivity of abnormal data identification is improved, the quality of valid housing attribute data is enhanced, the stability and reliability of asset valuation are strengthened, and calculation bias is reduced.

[0063] In some embodiments, the effective housing attribute data, preset asset static parameters, and preset asset dynamic parameters are subjected to multi-factor fusion processing to obtain target housing asset value data, including: performing interactive feature generation processing on the effective housing attribute data to obtain the cross coefficients corresponding to the effective housing attribute data; fusing the cross coefficients and preset asset static parameters to obtain static correction values; and further fusing the static correction values ​​and preset asset dynamic parameters to obtain target housing asset value data.

[0064] Specifically, the cross coefficients corresponding to valid housing attribute data can be numerical parameters obtained through interaction feature generation and processing, used to quantify the degree of influence of the interaction between different housing attributes on value. For example, the combination of room area and orientation can produce a value influence that is different from the sum of the independent effects of the two. The cross coefficients can be used to characterize the strength and direction of the above-mentioned interaction effects.

[0065] Furthermore, interactive feature generation can be achieved by combining different valid housing attribute data and applying multiplication, polynomial expansion, or mapping rules based on domain knowledge, without limitation here, to generate cross coefficients that can be used to capture complex attribute relationships.

[0066] In addition, the fusion process can be a data processing procedure that combines the dynamic interaction between attributes represented by the cross coefficient with the static value benchmark represented by the preset asset static parameters. This can include, but is not limited to, weighted summation, product summation and / or other function operations. The static correction value can be an intermediate value assessment result obtained through the fusion process after initially incorporating the interaction between attributes on the basis of the static value benchmark. This static correction value can be used to characterize the value adjustment amount after considering the attribute synergy effect under the static framework.

[0067] In addition, the re-fusion process can be a data processing procedure that combines the static correction value with the preset asset dynamic parameters. This allows the value obtained under the static framework to be further associated with real-time or periodic dynamic factors, such as through multiplicative adjustment, conditional addition, or attenuation.

[0068] According to the technical solution provided in this disclosure, interactive feature generation processing is performed on valid housing attribute data, and cross coefficients are generated through multiplication or polynomial expansion to quantify the interaction between area and orientation attributes. The cross coefficients are then fused with preset asset static parameters to obtain a static correction value incorporating attribute synergy. The static correction value is then further fused with preset asset dynamic parameters, and the target housing asset value data is obtained through multiplication adjustment. This improves the ability to capture attribute interaction effects, enhances adaptability to complex housing characteristics, strengthens the accuracy and dynamic response level of asset pricing, and improves the accuracy and adaptability to market changes of the determined target housing asset value data.

[0069] In some embodiments, the housing attribute data is standardized to obtain standardized housing attribute data, including: performing missing value completion processing on the housing attribute data to obtain completed attribute data; and performing format standardization processing on the completed attribute data to obtain standardized housing attribute data.

[0070] Specifically, missing value completion processing can be a data processing procedure that identifies missing fields in data records and fills in reasonable values ​​based on preset rules or algorithms. It can be used to eliminate incomplete records in housing attribute data, providing a structurally complete data foundation for subsequent unified calculations and analyses, and avoiding deviations in calculation results due to missing data. Among them, attribute data completion can fill in missing field values, so that all necessary attribute fields of each housing record have valid values. For example, if the orientation information of a room on a certain floor is missing, it can be inferred and completed based on the general orientation of other rooms on the same floor of the building or the architectural design drawings; if the vacancy period is missing, it can be calculated based on the most recent status change time recorded by the system and the current time.

[0071] Further, the specific methods for handling missing value imputation include, but are not limited to, for numerical fields such as area and vacancy duration, the average or median values of similar rooms in the same building or on the same floor can be used for filling; for categorical fields such as orientation and rental status, the most likely value of the property or the value with the highest frequency in the data can be used for filling; for complex situations that cannot be imputed by simple rules, they can be temporarily marked and entered after manual verification.

[0072] For example, in the management of centralized apartment properties, the property data collected from different buildings includes records of rooms 2105, 2108, etc. If the "orientation" field value of room 2105 is empty and the "vacancy duration" field value of room 2108 is empty, then missing value imputation can be performed. For room 2105, query the orientations of other entered rooms on the F1 floor of building 2 where it is located, and confirm that the majority is "north", so the orientation of room 2105 can be filled as "north"; for room 2108, by querying the rental status change log, the date when it last changed to the "pending rental" status, calculate the number of days from this date to the current date, and obtain a vacancy duration of "100 days" and fill it into the corresponding field to form the imputed property data.

[0073] In addition, format standardization processing can be a data processing process that unifies and standardizes the representation formats, units, encodings, etc. of data fields. In this way, the imputed property data from different sources and in different formats can be converted into standard format data with a unified specification that can be recognized and calculated, ensuring the consistency of model input, avoiding calculation errors or inefficiencies caused by format differences, and thus obtaining standardized housing property data.

[0074] Further, format standardization processing can also include multiple aspects. For example, the area unit can be unified as "square meters", the vacancy duration unit can be unified as "days", the text descriptions of orientations (such as "south", "south facing", "South") can be unified into preset enumerated values (such as "S" representing south), the Chinese character "building" in the building number can be removed or the format can be unified, and it is ensured that the rental status field is a boolean value (such as "1" can be used to represent rented, "0" can be used to represent pending rental), etc.

[0075] Furthermore, format standardization can also include data type conversion, such as converting text-formatted numeric strings into numeric types for calculation, and adjusting field order. For example, format standardization can convert room area values ​​from the string "30.5 square meters" with units to the pure numeric value "30.5"; it can extract the vacancy duration from the string "vacant for 100 days" and convert it to the integer value "100"; it can map orientation information from different texts such as "north-facing" and "facing north" to the standardized code "N"; and it can format building information from "Building 1" and "Building 2" to "B1" and "B2", thus standardizing the data format of all attribute fields in each property record and generating standardized housing attribute data. For example, the standardized housing attribute data corresponding to the record of room 2108 can be {Building: "B2", Floor: "F1", Room Number: "2108", Area: 35.0, Orientation: "S", Rental Status: 0, Vacancy Duration: 100}.

[0076] According to the technical solution provided in this disclosure, by filling missing numerical fields with the average value of the same building or floor, and filling missing categorical fields with the most frequent or most likely values, complete attribute data is obtained. The completed attribute data is then standardized in format, unifying the area unit to "square meters," the vacancy period unit to "days," the orientation text to a preset enumeration value, and the building number to format. Data type conversion and field order adjustment are also performed to output standardized housing attribute data. This improves the completeness and consistency of housing attribute data, enhances the accuracy and reliability of asset valuation, ensures the completeness of each property record through missing value completion, eliminates potential interference or interruption to subsequent price calculations due to data loss, and unifies the format differences between multi-source data through format standardization, simplifies data reading and parsing logic, and improves processing efficiency and reliability.

[0077] In some embodiments, after sending the target asset value data of the house to the target terminal device for display, the method further includes: obtaining real-time feedback data; performing closed-loop self-learning processing on the real-time feedback data and the target house asset value data to obtain asset dynamic update parameters; and performing multi-factor fusion processing on the asset dynamic update parameters, valid house attribute data, and preset asset static parameters to obtain house asset value update data.

[0078] Specifically, real-time feedback data can be dynamic response information related to value data collected from the actual process of leasing transactions. This real-time feedback data can serve as evidence of reactions and behaviors to initial pricing, and can also be used to assess and calibrate the accuracy of price generation. This real-time feedback data can be recorded and uploaded through the leasing business system or user terminal devices. For example, when tenants browse property listings and conduct operations such as inquiries, scheduling viewings, signing contracts, or abandoning transactions through terminal devices, the operation behavior and its associated time, frequency, and / or result information can be recorded. This is not limited here, and can be used to characterize the market's acceptance of the current pricing and changes in preferences.

[0079] Furthermore, real-time feedback data may also include the strength of rental intentions, comparisons of browsing time for different price ranges, and supplementary information from market research conducted by rental personnel, etc., without limitation here.

[0080] In addition, closed-loop self-learning processing can be an iterative optimization process that compares and analyzes target housing asset value data with actual market feedback (real-time feedback data) and adjusts parameters or rules. Closed-loop self-learning processing can build a mechanism that can self-correct and learn based on actual market performance, thereby improving adaptability. Among them, the asset dynamic update parameters can be a set of values ​​obtained through closed-loop self-learning processing that can be used to adjust the weights or coefficients of various influencing factors. The changes in the values ​​of these asset dynamic update parameters can be used to characterize the reassessment of the importance of various pricing factors by market dynamics.

[0081] Furthermore, multi-factor fusion processing can be a data processing procedure that calculates multiple parameters or datasets from different sources and dimensions according to specific fusion rules or models. It can be achieved by combining asset dynamic update parameters, valid housing attribute data, and preset asset static parameters, through methods such as weighted averaging, factor recalibration, and / or constructing new fusion models. For example, asset dynamic update parameters can be used as adjustment coefficients to apply to the base value calculated from valid housing attribute data and preset asset static parameters. The housing asset value update data can be the housing asset value data that is recalculated after adjusting the dynamic update parameters by incorporating real-time market feedback information, and is more in line with the current market conditions.

[0082] For example, in an apartment management system, a data collection program can continuously run in the background to obtain real-time feedback data from the apartment rental platform. This includes recording tenant clicks and browsing details for different priced listings, calculating the number of viewing appointments received for each listing within a set time window, and linking this to the lease agreement signing status. Through closed-loop self-learning, the initial price can be correlated with the real-time market performance of the corresponding listing (e.g., low appointment volume for high-priced listings and rapid sales for low-priced listings). Algorithms can identify potential biases in factor weights that may be out of touch with the market; for example, the weight of the "south-facing" factor might be overestimated in the current market environment. If the price reduction for properties vacant for more than 60 days is insufficient, a set of dynamic asset update parameters can be generated. For example, it can be suggested to lower the coefficient of the south-facing factor from 1.2 to 1.1 and increase the acceleration coefficient of price reduction for long-vacant properties. Through multi-factor fusion processing, the dynamic asset update parameters can be recalculated with the property's inherent valid housing attribute data (such as room 2107 being 30 square meters and south-facing) and preset static asset parameters (such as the basic rent per square meter in the area) to obtain updated property asset value data. For example, the recommended rental price for room 2107 can be updated from 3,000 yuan per month to 2,900 yuan per month.

[0083] According to the technical solution provided in this disclosure, by acquiring real-time feedback data, the real-time feedback data and the target housing asset value data are processed through closed-loop self-learning. The deviation between market reaction and initial pricing is compared, and asset dynamic update parameters are output. The asset dynamic update parameters, effective housing attribute data, and preset asset static parameters are subjected to multi-factor fusion processing. The updated housing asset value data is obtained by recalculating through weighted average or factor recalibration. In this way, the adaptability to real-time market changes is improved, the timeliness and accuracy of asset value data are enhanced, and the scientific nature of leasing decisions is strengthened.

[0084] In some embodiments, sending the target property asset value data to a target terminal device for display includes: performing message encapsulation processing on the target property asset value data to obtain encapsulated data; performing encoding processing on the encapsulated data to obtain encoded data; and sending the encoded data to the target terminal device for display through a preset secure channel.

[0085] Specifically, message encapsulation processing can be a data processing process that combines payload data (i.e., target property asset value data) with necessary control information (such as source address, destination address, data packet sequence number, check information, etc.) into a complete data transmission unit (i.e., message) through a predetermined communication protocol format. This can standardize the data format and ensure that the target property asset value data can be correctly identified, routed and reassembled during network transmission.

[0086] Furthermore, this message encapsulation process can be based on relevant protocols in the TCP / IP family, such as the Transmission Control Protocol / Internet Protocol (TCP / IP), to package asset value data and its metadata and add corresponding message headers. The encapsulated data is a data packet with a standard format to be transmitted, formed through message encapsulation processing.

[0087] Furthermore, encoding processing can convert encapsulated data from one form to another, or it can convert the encapsulated binary data stream into a signal form suitable for transmission over a specific secure channel, and can incorporate encryption or compression logic during the conversion process.

[0088] Furthermore, the encoding process includes, but is not limited to, encryption encoding based on Secure Sockets Layer / Transport Layer Security Protocol (SSL / TLS) to convert the encapsulated data into ciphertext; or encoding based on 64-bit printable character encoding (Base64) and other formats to adapt to the corresponding text protocol transmission; or compression encoding to reduce network bandwidth usage. The encoded data can be in the form of data that has been transformed through encoding processing and is ready to enter the physical transmission channel.

[0089] Furthermore, the preset secure channel can be a pre-established and configured communication link with data encryption and authentication mechanisms. This preset secure channel can be used to ensure the confidentiality, integrity, and non-repudiation of encoded data during transmission, preventing the encoded data from being stolen or tampered with during transmission. This technical feature refers to communication infrastructure independent of the data content.

[0090] According to the technical solution provided in this disclosure, the target property asset value data and its metadata are packaged into encapsulated data with a message header using the TCP / IP protocol; encoded data is formed through SSL / TLS encryption and Base64 encoding; and sent to the target terminal device through a preset secure channel. This improves the standardization and integrity of the data during transmission, enhances the reliability of information encryption, and strengthens the security of asset value data display. By introducing message encapsulation processing, the asset value data is standardized into network transmission units, ensuring the identifiability and manageability of the data in complex network environments. Encoding the encapsulated data adapts to the transmission requirements of the secure channel, improving the efficiency and security of data transmission.

[0091] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this disclosure, and will not be described in detail here.

[0092] Figure 3 This is a schematic diagram of another method for determining asset value data provided in this disclosure. Figure 3 As shown, the method for determining the asset value data includes: The determination of asset value data disclosed herein mainly includes the stages of "acquiring housing information, integrating and processing information, generating rental prices, and applying rental business." The following is a detailed explanation using a specific application scenario, an apartment rental platform: 1. Obtaining Housing Information (Taking multi-building floors and rooms as an example) Hardware and data sources: Information is obtained through the apartment management system's property information input terminal (operated by staff or connected to smart meters, water meters, etc., to indirectly determine the occupancy status of the property and whether it is rented out), and physical identification of the property (such as room number codes and building / floor spatial layout data). For example, for rooms in different buildings such as Building 1-F1, Building 2, etc., information such as room number (e.g., 2104, 2105), building (e.g., Building 1, 2), floor (e.g., F1, F2), area (can be pre-measured and entered), and orientation (e.g., south, north) is collected. The system calculates the vacancy duration (e.g., "vacant for 1 day," "vacant for 100 days") based on the recorded rental status and vacancy start time to determine whether the property is rented out (status such as rented, available for rent, etc.).

[0093] Processing logic: Based on preset rules, data (house attribute data) can be retrieved from various data sources at regular intervals or in real time, and standardized and validated for reasonableness (such as whether the room number coding format is correct and whether the vacancy period calculation is reasonable). The standardized information is then transmitted to the information integration database.

[0094] 2. Information integration and processing Database Construction: Establish a housing information database, storing information categorized by building, floor, room number, and other dimensions. Record information in the form of fields such as: building number (Building 1, Building 2, etc.), floor identifier (F1, F2, etc.), room number (2104, 2105, etc.), area value, orientation (South, North), rental status (Rented, Available for Rental), and vacancy duration (numerical value and unit). Simultaneously, establish relationships, such as the affiliation of different floors within the same building and different rooms on the same floor.

[0095] Information association and update: When new information (such as a change in the rental status of a room or an increase in vacancy time) is received, the database triggers an update mechanism to ensure that information in all dimensions is associated and synchronized in real time, providing an accurate data foundation for subsequent price generation.

[0096] 3. Rental Price Generation Model Construction Approach: A price generation model is constructed based on multi-dimensional housing information. Influencing factors and their weights are defined, such as: Basic factors: Area (the larger the area, the higher the base price, weighted W1), Orientation (south-facing is the best orientation, weighted W2, north-facing and other orientations are assigned coefficients according to their differences).

[0097] Dynamic factors: vacancy duration (the longer the vacancy, the lower the price adjustment coefficient to speed up leasing, weighted by W3; if the vacancy exceeds 30 days, the price will be reduced by a certain percentage for every additional 10 days), and whether it is rented (the status of being available for rent is included in the price calculation, and the status of being rented can be used for historical price reference).

[0098] Simplified Formula Example: Rental Price = (Base Price + Area × W1 + Orientation Coefficient × W2) × (1 - Vacancy Duration Adjustment Coefficient × W3). The base price can be referenced from the average base price of similar properties in the regional market, and adjusted in combination with apartment brand, services, etc.; the orientation coefficient, such as 1.2 for south-facing and 0.8 for north-facing, is set according to actual market preferences.

[0099] Model Application: Integrate information from the housing data in the database, substitute fields into the model, and calculate the rental price for each property. For example, room 2107 (area assumed to be S, south-facing), vacant for 30 days, is priced according to the model; room 2105 (north-facing, vacant for 1 day) is priced according to the corresponding factors.

[0100] 4. Application of Leasing Business Property Display and Recommendation: On rental platforms (such as online viewing systems), properties are displayed based on generated rental prices. Combined with property information (orientation, vacancy duration, etc.), the system provides tenants with filtering and sorting functions. For example, if a tenant selects a south-facing room with a short vacancy period, the system will sort and recommend properties by price, vacancy duration, etc., prioritizing properties with high matching rates and reasonable prices.

[0101] Rental decision support: Rental staff can communicate with tenants based on the price and property information generated by the system, quickly respond to tenants' questions about prices and property status, and improve rental transaction efficiency; at the same time, they can adjust operational strategies according to different property prices and rental status (such as launching promotional activities for properties that have been vacant for a long time, and setting reasonable discounts based on the price model).

[0102] According to the technical solution provided in this disclosure, it breaks through the limitations of traditional methods that only focus on basic physical information. It comprehensively integrates information such as building, floor, room number, area, orientation, whether it is rented, and vacancy duration to establish a related database, enabling real-time synchronization and dynamic updates of information, and providing an accurate data foundation for the entire rental process. It introduces dynamic factors such as vacancy duration and combines them with basic factors such as area and orientation. By setting reasonable weights and adjustment coefficients, a dynamic price calculation model is constructed, so that the rental price reflects both the value of the property and adapts to operational needs (accelerating the rental of vacant properties and ensuring the income of high-quality properties), which is different from the traditional fixed and single-factor pricing method. It integrates accurate information and scientific pricing throughout the entire process of property display, tenant matching, and rental decision-making, achieving efficient matching of property and tenant needs and improving the efficiency and effectiveness of the rental business.

[0103] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0104] Figure 4 This is a schematic diagram of an asset value data determination device provided in an embodiment of this disclosure. Figure 4 As shown, the asset value data determination device includes: The first processing module 401 is used to standardize the house attribute data to obtain standardized house attribute data. The second processing module 402 is used to perform rationality verification processing on the standardized housing attribute data to obtain valid housing attribute data; The third processing module 403 is used to perform multi-factor fusion processing on the valid house attribute data, the preset static parameters of the asset, and the preset dynamic parameters of the asset to obtain the target house asset value data. The fourth processing module 404 is used to send the target asset value data of the house to the target terminal device for display.

[0105] According to the technical solution provided in this disclosure, by inputting standardized housing attribute data, the data is sequentially cleaned, converted, formatted, and validated to map it into standardized housing attribute data. This data undergoes a rationality check, which involves filling in missing fields through data integrity checks, removing outliers through numerical range checks, and clearing mutually exclusive records of rental status and vacancy duration through logical consistency checks, outputting valid housing attribute data. This valid housing attribute data is then fused with preset static and dynamic asset parameters through multi-factor fusion processing, followed by weighted calculation and coefficient adjustment to obtain the target housing asset value data. Finally, the target housing asset value data is packaged and sent to the target terminal device for display. This improves the consistency and standardization of heterogeneous data, enhances data quality and authenticity, strengthens the responsiveness of asset valuation to market dynamics, improves the accuracy and comparability of pricing results, and increases decision-making transparency and business execution efficiency.

[0106] In some embodiments, the third processing module 403 is specifically used to perform dynamic weight mapping processing on the valid house attribute data to obtain a dynamic weight vector; perform weighted fusion processing on the dynamic weight vector and preset asset static parameters to obtain an asset value fusion value; and perform reweighted processing on the asset value fusion value and preset asset dynamic parameters to obtain target house asset value data.

[0107] In some embodiments, the second processing module 402 is specifically used to: perform historical valuation alignment processing on standardized housing attribute data to obtain housing asset estimates; perform difference calculation processing on the housing asset estimates and standardized housing attribute data to obtain verification results; and perform filtering processing on the standardized housing attribute data based on the verification results to obtain valid housing attribute data.

[0108] In some embodiments, the third processing module 403 is specifically used to perform interactive feature generation processing on the valid house attribute data to obtain the cross coefficients corresponding to the valid house attribute data; to fuse the cross coefficients with preset asset static parameters to obtain static correction values; and to fuse the static correction values ​​with preset asset dynamic parameters to obtain target house asset value data.

[0109] In some embodiments, the first processing module 401 is specifically used to perform missing value completion processing on the house attribute data to obtain completed attribute data; and to perform format standardization processing on the completed attribute data to obtain standardized house attribute data.

[0110] In some embodiments, the asset value data determination device is further configured to: acquire real-time feedback data; perform closed-loop self-learning processing on the real-time feedback data and the target house asset value data to obtain asset dynamic update parameters; and perform multi-factor fusion processing on the asset dynamic update parameters, valid house attribute data, and preset asset static parameters to obtain house asset value update data.

[0111] In some embodiments, the fourth processing module 404 is specifically used to: encapsulate the target housing asset value data to obtain encapsulated data; encode the encapsulated data to obtain encoded data; and send the encoded data to the target terminal device for display through a preset secure channel.

[0112] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this disclosure.

[0113] Figure 5 This is a schematic diagram of the electronic device 5 provided in an embodiment of this disclosure. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program 503, it implements the steps in the various method embodiments described above. Alternatively, when the processor 501 executes the computer program 503, it implements the functions of each module / unit in the various device embodiments described above.

[0114] Electronic device 5 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 5 may include, but is not limited to, processor 501 and memory 502. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown, or different components.

[0115] The processor 501 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0116] The memory 502 can be an internal storage unit of the electronic device 5, such as a hard disk or RAM of the electronic device 5. The memory 502 can also be an external storage device of the electronic device 5, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the electronic device 5. The memory 502 can also include both internal and external storage units of the electronic device 5. The memory 502 is used to store computer programs and other programs and data required by the electronic device.

[0117] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0118] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a readable storage medium (e.g., a computer-readable storage medium). Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0119] The above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. A method for determining asset value data, characterized in that, include: Standardize the housing attribute data to obtain standardized housing attribute data; The standardized housing attribute data is subjected to a rationality verification process to obtain valid housing attribute data; The effective housing attribute data, preset asset static parameters, and preset asset dynamic parameters are subjected to multi-factor fusion processing to obtain the target housing asset value data. The target asset value data of the house is sent to the target terminal device for display.

2. The method for determining asset value data according to claim 1, characterized in that, The process of performing multi-factor fusion processing on the valid housing attribute data, preset asset static parameters, and preset asset dynamic parameters to obtain target housing asset value data includes: The valid house attribute data is subjected to dynamic weight mapping processing to obtain a dynamic weight vector; The dynamic weight vector and the preset static asset parameters are weighted and fused to obtain the asset value fusion value; The asset value fusion value and the preset asset dynamic parameters are reweighted to obtain the target house asset value data.

3. The method for determining asset value data according to claim 1, characterized in that, The process of performing a rationality check on the standardized housing attribute data to obtain valid housing attribute data includes: The standardized housing attribute data is subjected to historical valuation alignment processing to obtain the estimated housing asset value; The difference between the estimated housing asset value and the standardized housing attribute data is calculated to obtain the verification result. The standardized house attribute data is filtered based on the verification results to obtain the valid house attribute data.

4. The method for determining asset value data according to claim 1, characterized in that, The process of performing multi-factor fusion processing on the valid housing attribute data, preset asset static parameters, and preset asset dynamic parameters to obtain target housing asset value data includes: The effective house attribute data is processed to generate interactive features to obtain the cross coefficients corresponding to the effective house attribute data. The cross coefficient and the preset asset static parameters are fused together to obtain a static correction value; The static correction value and the preset asset dynamic parameters are then fused together to obtain the target house asset value data.

5. The method for determining asset value data according to claim 1, characterized in that, The standardization process for the housing attribute data, resulting in standardized housing attribute data, includes: The missing value is filled in the house attribute data to obtain the completed attribute data; The completed attribute data is formatted and standardized to obtain the standardized house attribute data.

6. The method for determining asset value data according to any one of claims 1-5, characterized in that, After sending the target asset value data of the house to the target terminal device for display, the method further includes: Obtain real-time feedback data; The real-time feedback data and the target house asset value data are subjected to closed-loop self-learning processing to obtain asset dynamic update parameters. The asset dynamic update parameters, the valid house attribute data, and the preset asset static parameters are subjected to multi-factor fusion processing to obtain the house asset value update data.

7. The method for determining asset value data according to any one of claims 1-5, characterized in that, The step of sending the target asset value data of the house to the target terminal device for display includes: The target property asset value data is processed by message encapsulation to obtain encapsulated data; The encapsulated data is encoded to obtain encoded data; The encoded data is sent to the target terminal device for display through a preset secure channel.

8. An asset value data determination device, characterized in that, include: The first processing module is used to standardize the house attribute data to obtain standardized house attribute data. The second processing module is used to perform a rationality verification process on the standardized house attribute data to obtain valid house attribute data; The third processing module is used to perform multi-factor fusion processing on the effective house attribute data, preset asset static parameters and preset asset dynamic parameters to obtain the target house asset value data. The fourth processing module is used to send the target asset value data of the house to the target terminal device for display.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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